function error = fun(x,inputnum,hiddennum,outputnum,net,inputn,outputn)

w1=x(1:inputnum*hiddennum);

B1=x(inputnum*hiddennum+1:inputnum*hiddennum+hiddennum);

w2=x(inputnum*hiddennum+hiddennum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum);

B2=x(inputnum*hiddennum+hiddennum+hiddennum*outputnum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum);

tmp=[0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1];

out=[0 1;];

net=newff(tmp,out,7,{},'traingd');%构建网络

net.trainParam.goal=0.00001; %设定训练误差

net.trainParam.epochs=100; %设定最大训练步数;

net.trainParam.show=10; %每间隔100步显示一次训练结果

net.trainParam.lr=0.05; %学习速率0.05

net.trainParam.lr_inc = 1.05;

net.iw{1,1}=reshape(w1,hiddennum,inputnum);%隐层权值

net.lw{2,1}=reshape(w2,outputnum,hiddennum);%输出层权值

net.b{1}=reshape(B1,hiddennum,1);%隐层阈值

net.b{2}=B2;%输出层阈值

disp('start training');

[net]=train(net,inputn,outputn); %训练网络

disp('finish traning');

ann=sim(net,inputn);

disp('finish sim');

error=sum(abs(ann-outputn));

disp(error);